计算机工程与科学2026，Vol.48Issue(8)：1457-1469,13.DOI:10.3969/j.issn.1007-130X.2026.08.012
基于深度学习的跨语言检索技术研究
Research of deep learning-based cross-lingual retrieval techniques
曾广福 1黄明铭 1陆国锋 2王昊鹏3
作者信息
- 1. 国防科技大学计算机学院,湖南 长沙 410073
- 2. 联勤保障部队第921医院,湖南 长沙 410073
- 3. 西北师范大学计算机科学与工程学院,甘肃 兰州 730070
- 折叠
摘要
Abstract
In recent years,the explosive growth of online information has triggered extensive atten-tion to cross-lingual information retrieval methods.Deep learning-based cross-lingual retrieval models possess advantages such as the ability to learn the deep feature patterns of languages and good generali-zation capabilities,but they also face challenges such as poor initial ranking performance and insufficient relevance of top results.To further enhance the accuracy and response efficiency of deep learning-based cross-lingual retrieval,this paper takes the typical BLADE(bilingual lexical and document expansion)model as the foundation,and improves the deep learning-based cross-language retrieval model based on re-ranking principles while enhancing the initial ranking of the BLADE model.Finally,the future devel-opment trends of cross-lingual retrieval are prospected.关键词
深度学习/初排序/重排序/融合模型Key words
deep learning/initial ranking/re-ranking/fusion model分类
信息技术与安全科学引用本文复制引用
曾广福,黄明铭,陆国锋,王昊鹏..基于深度学习的跨语言检索技术研究[J].计算机工程与科学,2026,48(8):1457-1469,13.
